在本研究中主要探討於遠距離規模下基因片段交互作用的情況，並且運用網路拓撲分析其表現模式和生物性功能。網路特性能夠有效率地測量圖論中節點的重要性，以及節點彼此之間的關聯性，藉此辨識在生物系統裡的中心元素。本研究應用各種網路拓撲方法分析高通量染色體結構捕獲連結網路，然後結合 t-SNE 和 Network Embedding 進行資料分群。此外，HiCONET 是針對 Hi-C 資料提供連結熱圖和網路結構視覺化的服務平台。圖形化介面可以協助使用者在視覺上搜尋特定資料模式，同時連結熱圖與網路圖中相關聯的資料內容。借助 R Shiny 平台，使用者能夠透過點選視覺化結果和調整參數，互動式地探索其感興趣的資料範圍。此網路服務平台的網址是 https://changlab.shinyapps.io/hiconet/。

In this work we explore the interactions of gene regions in long-range scale with network topologies for analyzing expression patterns and biological functionalities. Network features help us efficiently measure the significance of nodes and relationships between other nodes, in order to identify the central elements in a biological system. We apply different network topological measures in analyzing Hi-C contact network, then use t-SNE and network embedding method for clustering. Furthermore, we developed a web server to visualize Hi-C data by contact map and network, HiCONET. The graphical interface lets users visually search for patterns in the Hi-C data, as simultaneously plotting related genomic region among contact map and network. Besides, users can interactively explore interesting regions through clicking network or selecting parameters of Hi-C data thanks to R Shiny platform. The server is free available in https://changlab.shinyapps.io/hiconet/.

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